Neural Information Processing 2018/2019

Here you can find the relevant content for Neural Information Processing 2018/2019. This unit covers several aspects of information processing in the brain, such as sensory processing, probabilistic codes, deep learning, recurrent neural networks, credit assignment, reinforcement learning and model-based inference.

It is jointly taught by Conor Houghton, Rui Ponte Costa and Cian O'Donnell at the Department of Computer Science [School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics], Faculty of Engineering, University of Bristol.

Recommended reading:

This field is highlight interdisciplinary, as such there is no single textbook that covers all our lectures. However, below we highlight with ** the most relevant ones for this unit.

Theoretical neuroscience:

1. ** Theoretical Neuroscience by P Dayan and L F Abbott (MIT Press 2001), see also errata.
   
2. Neuronal Dynamics by Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski. Full version online.
   
3. Introduction To The Theory Of Neural Computation, Volume I by John Hertz. (Classical and accessible book on neural computation)
   
4. Bayesian Brain: Probabilistic Approaches to Neural Coding

Machine/statistical Learning:

4. ** General ML book: Information Theory, Inference and Learning Algorithms by David MacKay. Full version available online
   
5. ** Deep Learning (including Recurrent neural nets): Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville
   
6. Unsupervised learning: Natural Image Statistics by Aapo Hyvarinen, Jarmo Hurri, and Patrik O. Hoyer. Full version available online.
   
7. Reinforcement learning: Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto. Full version available online.
   
   

** Super useful math/stat cheat-sheet by Iain Murray:
https://homepages.inf.ed.ac.uk/imurray2/pub/cribsheet.pdf

Recommended reading by lectures:

Conor:
Lecturer 1-3: Information theory (1,4 and lecturer notes)
Lecturer 4-5: Statistical theory (1,4 and lecturer notes)
Lecturer 6-7,9: Probabilistic brain (1,4 and lecturer notes)

Lecturer 8: Guest lecture

Rui: Neural circuits and learning
Lecturer 10: Different forms of learning (1,4)
Lecturer 11-12: Visual System: conv nets and backprop (5,6)
Lecturer 13: Sparse coding and autoencoders (5,6)
Lecturer 14: Reinforcement Learning: TD-learning, Q-Learning, Deep RL (1,7)
Lecturer 15-16: Auditory cortex, Recurrent neural networks, gated RNNs (1,4)

Cian:
Lecturer 17-18: Neural Data Analysis